please give a recommendation.....Course Title: Application of Statistics

Course Code: MSMG$-5100$H$-$A$-$WO$1-2024$GW$-$OSH

Group Members

Name

Email

Student Number

Margaret Anigabor

margaretanigabor@trentu.ca

$0777617$

$$Contents

$$

$$

$$

$$

$$

$$

INTRODUCTION

The leadership team of Store$24,$ comprising the CEO, president, CFO, and COO is faced with the decision to increase store$-$level retention, they are exploring a range of strategies that span wages and bonuses increments, training enhancements to career development initiatives. However, before implementing any of these strategies, they recognize the urgent need to develop a comprehensive understanding of company data. Specifically, they aim to discern the intricate relationship between employee tenure and store$-$level performance, as well as the financial consequences of increasing the length of time managers and crew members stay with the company.

This report aims to carry out a thorough analysis to establish the relationship between staff tenure and store profitability or performance. Moreover, other variables such as competitor information, local population count or statistics, store visibility measures, pedestrian statistics, geographical location, and operational hours have been incorporated to avoid result bias.

Through the detailed analysis, actionable strategies that align with the company$$s goal have been recommended to provide Store$24$ leadership with the knowledge and tools necessary to make well$-$informed decisions that will strengthen employee retention and drive the company toward sustained growth and success.

ANALYSIS

Methodology

Data Gathering and Review

The comprehensive analysis involves gathering essential data from a diverse sample of $75$ stores, Table $1$ below presents the variables from the $75$ stores along with their descriptions. A white text with black text

Description automatically generated

Table $1$: Variables and their descriptions

Identification of Variables

The dependent and independent variables are identified, and all the necessary data is incorporated into the analysis in other to avoid result bias and the overestimation of the impact of the employee tenure on the store profits.

Dependent Variable $-$Profit

Independent variables $-$Manager tenure, Crew Tenure, Competition, Population, Visibility,

Pedestrian Count, Hours$24,$ Residential

Identify similarities and Differences in the data set

A screenshot of a computer

Description automatically generated

Figure $1\mathrm{.}1$: Top $10$ and lowest $10$ performers

Before we delve into the multiple regression models to understand the relationships each variable has on Store$24$ performances, we provide an extensive view of the data gathered by Hart on the top$-$performing stores versus the lower$-$performing stores in $2000.$ In addition to Hart$$s explication of the top ten most profitable stores the managers and crew tenure was almost four times the level of manager and crew tenure in the least profitable stores. Figure $1\mathrm{.}1$ highlights a few distinctions between the top$-$performing stores and the least profitable stores these included that lower$-$performing stores had almost double to competition with lower visibility ratings than that of higher$-$performing stores. Furthermore, there is a huge gap in the population as the higher$-$performing store's population excessively exceeded those of the lower$-$performing stores. While Hart$$s analysis may be a significant factor in explaining the difference in performance in these stores; the variables highlighted in Figure $1\mathrm{.}1$ also describe why there may be a significant difference in the performance. In contrast to the differences highlighted in Figure $1\mathrm{.}1,$ we were able to identify a few similarities such as stores operating in areas with smaller populations have a higher manager tenure than stores within higher populated areas.

Even though the analysis thus far has provided valuable information, there are still numerous questions that need to be answered, as such multiple regression models were conducted to understand the different variables affecting the performance of each store from the data provided by Jenkins.

Regression Model $1$

Our main priority is to define the relationship between the store's financial performance and employee tenure, which will signify the importance of implementing mechanisms to improve employee retention. The first regression model illustrated in Figure $1\mathrm{.}2$ showcases the relationship between store pro